MATERIAL CONDITIONS PROGRESS AND PUZZLES IN MEASUREMENT

Angus Deaton, Princeton University 4th OECD World Forum, Delhi, October 16th, 2012

This talk

Measurement in three areas Material well-being: purchasing power price-

adjusted incomes for international comparisons Understanding PPPs Implications for global poverty & inequality Outstanding puzzles

Information gaps in international data Material wellbeing and broader measures of

wellbeing How do they fit together

National accounts

Remain as central as ever Pillars on which everything else depends New frontiers exciting, but much remains to be done with the old We must not neglect

Refocusing towards well-being GNP and NNP better than GDP Drilling down: not only aggregates but who received what Rethinking imputations Tension between comprehensibility and completeness Examples: FISIM and “actual” consumption

Lots of work to be done here Extensions to what we have now As well as improving NAS in many countries where they are

weak

PRICE INDEXES AND GLOBAL INCOME COMPARISONS

PPP exchange rates

The Penn World Table is the basis for the income comparisons in almost all global calculations Or other numbers based on ICP price collection

The ICP collects prices of individual goods around the world Rounds in 1993-1995: PWT 6.X 2005: PWT 7.0 2011 (not yet released)

Used to construct multilateral price indexes Which deflate local estimates of income, consumption, etc.

measured in local currencies Between rounds, and waiting for rounds, e.g. post 2005 Local CPIs (or implicit price deflators) are used to update PPP between, say, US and India, should track relative growth

rates of CPIs in US and India

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6 7 8 9 10 11 Logarithm of per capita GDP in 2005 international $

Congo, DR

Burundi

Sao Tome & Principe

Cape Verde

Lesotho Guinea

Ghana

Cambodia Togo

Guinea Bissau

India

Philippines China Namibia

Tonga

Yemen Congo, R Lebanon

Gabon Kuwait

Fiji

Nigeria

Tanzania Angola

Bolivia

Ethiopia

Vietnam

Bangladesh

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Revisions down for oil producers and high oil prices in 2005

Large upward revisions for many low income countries

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1960 1970 1980 1990 2000 2010 year

WDI 2008, 2005 prices

WDI 2007, 1993 prices

PWT 5.6, 1985 prices

PWT 6.2, 1993 prices

Gini coefficient for per capita GDP, weighted by population

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1960 1970 1980 1990 2000 2010 year

WDI 2008, 2005 prices

WDI 2007, 1993 prices

PWT 5.6, 1985 prices

PWT 6.2, 1993 prices

Gini coefficient for per capita GDP, weighted by population

Why?

We do not understand, yet it is crucial for understanding changes in global income inequality Not important for global poverty, because $1 a day line is average

of poor country lines & depends only on PPP rates among poor countries, which are not much affect World Bank switch from $1 to $1.25 is an increase in the line, and

not consequence of PPP revision Note this is not Balassa-Samuelson BS says ER and CPI change at different rates with growth Because of non-traded goods, and rising productivity in traded-

goods sector But PPPs are updated by CPIs, not exchange rates

Number of technical, tedious, but important details E.g. treatment of trade-balance differs in ICP and NIPA Which is why oil exporters got to be relatively richer in 2005

National v international

Local price indexes use local weights to weight price changes

PPP comparing two or more countries uses weights from two or more countries

Rate of growth of PPP is not the same as differential rate of growth of CPIs Except under exceptional conditions

CPI updating between rounds will lead to discontinuities when new data become available As from 1985 to 1993 And 1993 to 2005

The aggregation bias causes largest revisions in fast growing relatively poor countries

Where are we now?

Data from the ICP2011 is currently in house Being cleaned and processed Final results in December 2013

Updating will be done at a disaggregated level Tested from 2005 to 2011 using data from 2005 Much higher quality than 1993 or earlier Methodology much improved

Since World Bank has been running this, overall quality much improved Will begin to resolve these issues By December 2013 some idea about world inequality

and world poverty

Precision of PPPs

How precise are PPPs and other numbers based on them? Large standard errors? Need a concept

Richard Stone (1949) “Why do we need to compare the U.S. with, say,

India or China? Everybody knows that one country is very rich and another country very poor, does it matter whether the factor is thirty or fifty or what?”

We have learned a lot since, or have we?

A star system of PPPs

Compared with a “star” country, e.g. US, each country has a N-vector of price relatives, one for each good

Variance of log price ratios between pairs of countries source of PPP index uncertainty

Theorem: log of Laspeyres to Paasche ratio for A relative to B (divided by N) is approximately equal to the variance over goods of log price ratios for A to B

Standard error of PPP comes from thinking about log price ratios as drawn from a distribution PPP distribution calculated using (fixed) weights applied to

price draws

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0 .05 .1 .15 .2 s.e. of log Tornquist or log Fisher

Tanzania

Tajikistan

Kyrgyzstan

Azerbaijan

Equatorial Guinea China

India

USA

Group is Canada, Austria, Germany Belgium, France, Finland, Luxemburg, Denmark, Britain, Ireland, Switzerland, Italy, Norway, Australia, Sweden, Iceland, Portugal, Spain, Netherlands, New Zealand, Slovenia, Greece, Cyprus, Israel, Chile (from left to right)

Notes on the figure

The standard errors are large 2 s.e. for China and India is around 30 percent

Much smaller for the group on the left But still substantial, ten percent

Stone again in 1949 “I do not expect a very rapid resolution of the intellectual

problems of making welfare comparisons between widely different communities”

But ICP indexes are multilateral, not bilateral, and we need standard errors for those Essence of multilateral is that transitivity is enforced, and

bilateral indexes are not transitive So what happens?

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s.e. of bilateral log Törnquist or log Fisher

Bahrain

Qatar

Kuwait

Tajikistan

Chad Fiji Saudi

Multilateral s.e.’s

Multilateral standard errors are typically larger Average 15 percent instead of 12 percent

Dispersion of ML standard errors smaller Transitivity is spreading the errors Poor bilateral is buttressed by ML comparisons

Close countries have much larger s.e.’s ML is a bad idea for them Bringing Tajikistan into the Canada US comparison is not

necessarily a good idea Middle group of countries where costs of transitivity

are balanced by the gains Still substantial uncertainty, big standard errors

INTERNATIONAL DATA

Bricks without straw

ICP and others accept national estimates of GDP PPP only deflates them by price indexes ICP offers some technical assistance Many countries national accounts remain very weak Particularly but not exclusively in Africa

Household surveys are inconsistent with national accounts Different growth rates of mean income/consumption Allows much mischief with poverty estimates Much more rapid poverty decline from NAS plus assumptions

about inequality than directly measured from surveys India is the classic but far from only case

International “drilling down” efforts in NAS statistics should help here

Multipurpose surveys should include health and income

Data gaps

International system has very little funding to collect new data Limited interest in funding governments to improve

systems No international control, in contrast to often

elaborate domestic controls International agencies produce numbers with little cross-

check: appearance of self-serving data production Compare domestic CPI and unemployment Perhaps high quality global statistics, like global justice, is a

cosmopolitan fantasy Certainly no political constituency that will punish

egregious errors/use of data for self-promotion Who needs these numbers?

MATERIAL V BROADER CONCEPTS OF WELLBEING

GDP v Happiness

Has GDP been replaced by happiness measures? Perhaps we don’t need income numbers of we can

measure happiness Happiness has everything that GDP has, except

for the mistakes! In particular , income doesn’t matter beyond some

point

We can ignore material well-being

New information

Gallup’s World Poll samples all of the citizens of the world 1,000 people in each country in each year 160 countries now covered, since 2006 Identical questionnaires

Data allow examination of many questions about SWB

Are richer countries better off? At least beyond some point

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Cantril’s ladder question

Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally stand at this time?

Life evaluation measure, like life satisfaction, but hedonically neutral

Not happiness Happiness proper (mood, hedonic experience) Did you experience a lot of happiness yesterday

Distinction between experiencing life (hedonics, like happiness, sadness, enjoyment, etc) and thinking about life is crucial, conceptually and empirically Different correlates, different adaptation, etc.

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0 10000 20000 30000 40000 50000 pc GDP, 2005 international constant $

Cantril Ladder, 2008

3 4

5 6

7 8

(mea

n) la

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0 10000 20000 30000 40000 50000 pc GDP, 2005 international constant $

Cantril Ladder, 2008

3 4

5 6

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800 3200 12800 51200 pc GDP, 2005 international constant $

Cantril Ladder, 2008

3 4

5 6

7 8

800 3,200 12,800 51,200

GDP per capita, 2005 international constant $

Togo Sierra Leone

Liberia

Burundi

Bangladesh

Afghanistan

Hong Kong

Japan

Denmark

Mexico

Brazil Guatemala

Colombia

Congo

Georgia

Hungary

Korea

Angola

Benin

Kenya

Mozambique

Costa Rica Panama

Venezuela

Finland Norway

Lebanon Pakistan

India

China

Argentina Germany

Singapore

Thailand

Aver

age

ladd

er sc

ore

Russia

GDP v life evaluation

Life evaluation is closely linked to per capita income True within countries as well as across countries

Growing evidence that this is true over time too No evidence that income is misleading about

wellbeing GDP has many problems of its own Well understood and good work going on to

improve it

Measuring wellbeing

Happiness measures also have many problems Becoming more apparent as we go

Different measures of “happiness” are very different things Are you happy ? How satisfied are you with your life? Which is the right one for policy? Very different policy correlated

Increasing evidence that people have great difficulty answering the questions in a coherent way Context effects are very important Cannot use to track over time Context effects change group rankings

Much work to be done here before these can form a firm basis for policy

THANK YOU!